Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations1296675
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory227.5 MiB
Average record size in memory184.0 B

Variable types

Numeric10
DateTime2
Text8
Categorical3

Alerts

Unnamed: 0 is highly overall correlated with unix_timeHigh correlation
lat is highly overall correlated with merch_latHigh correlation
long is highly overall correlated with merch_long and 1 other fieldsHigh correlation
merch_lat is highly overall correlated with latHigh correlation
merch_long is highly overall correlated with long and 1 other fieldsHigh correlation
unix_time is highly overall correlated with Unnamed: 0High correlation
zip is highly overall correlated with long and 1 other fieldsHigh correlation
is_fraud is highly imbalanced (94.9%)Imbalance
amt is highly skewed (γ1 = 42.27787379)Skewed
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
trans_num has unique valuesUnique

Reproduction

Analysis started2025-10-04 12:59:41.408104
Analysis finished2025-10-04 13:01:42.926289
Duration2 minutes and 1.52 second
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct1296675
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean648337
Minimum0
Maximum1296674
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2025-10-04T13:01:43.021890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile64833.7
Q1324168.5
median648337
Q3972505.5
95-th percentile1231840.3
Maximum1296674
Range1296674
Interquartile range (IQR)648337

Descriptive statistics

Standard deviation374317.97
Coefficient of variation (CV)0.57735094
Kurtosis-1.2
Mean648337
Median Absolute Deviation (MAD)324169
Skewness-5.1691189 × 10-15
Sum8.4068238 × 1011
Variance1.4011395 × 1011
MonotonicityStrictly increasing
2025-10-04T13:01:43.156195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12966741
 
< 0.1%
01
 
< 0.1%
11
 
< 0.1%
12966581
 
< 0.1%
12966571
 
< 0.1%
12966561
 
< 0.1%
12966551
 
< 0.1%
12966541
 
< 0.1%
12966531
 
< 0.1%
12966521
 
< 0.1%
Other values (1296665)1296665
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
12966741
< 0.1%
12966731
< 0.1%
12966721
< 0.1%
12966711
< 0.1%
12966701
< 0.1%
12966691
< 0.1%
12966681
< 0.1%
12966671
< 0.1%
12966661
< 0.1%
12966651
< 0.1%
Distinct1274791
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
Minimum2019-01-01 00:00:18
Maximum2020-06-21 12:13:37
Invalid dates0
Invalid dates (%)0.0%
2025-10-04T13:01:43.282520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:43.404345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cc_num
Real number (ℝ)

Distinct983
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1719204 × 1017
Minimum6.0416207 × 1010
Maximum4.9923464 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2025-10-04T13:01:43.533962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.0416207 × 1010
5-th percentile6.3048488 × 1011
Q11.8004295 × 1014
median3.5214173 × 1015
Q34.6422555 × 1015
95-th percentile4.497914 × 1018
Maximum4.9923464 × 1018
Range4.9923463 × 1018
Interquartile range (IQR)4.4622125 × 1015

Descriptive statistics

Standard deviation1.3088064 × 1018
Coefficient of variation (CV)3.1371798
Kurtosis6.1799499
Mean4.1719204 × 1017
Median Absolute Deviation (MAD)3.0764709 × 1015
Skewness2.851879
Sum-6.7255419 × 1018
Variance1.7129743 × 1036
MonotonicityNot monotonic
2025-10-04T13:01:43.677166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.512828415 × 10183123
 
0.2%
5.713652351 × 10113123
 
0.2%
3.672269902 × 10133119
 
0.2%
2.131124026 × 10143117
 
0.2%
3.54510934 × 10153113
 
0.2%
6.534628261 × 10153112
 
0.2%
6.011367958 × 10153110
 
0.2%
2.720433096 × 10153107
 
0.2%
6.011438889 × 10153106
 
0.2%
6.011109737 × 10153101
 
0.2%
Other values (973)1265544
97.6%
ValueCountFrequency (%)
6.041620718 × 10101518
0.1%
6.042292873 × 10101531
0.1%
6.042309813 × 1010510
 
< 0.1%
6.042785159 × 1010528
 
< 0.1%
6.048700208 × 1010496
 
< 0.1%
6.04905963 × 10101010
0.1%
6.049559311 × 1010518
 
< 0.1%
5.018029536 × 10111559
0.1%
5.018181333 × 10118
 
< 0.1%
5.018282048 × 1011515
 
< 0.1%
ValueCountFrequency (%)
4.992346398 × 10182059
0.2%
4.989847571 × 10181007
 
0.1%
4.980323468 × 1018532
 
< 0.1%
4.973530368 × 10181040
0.1%
4.958589672 × 10181476
0.1%
4.95682899 × 10182566
0.2%
4.911818931 × 10189
 
< 0.1%
4.906628656 × 10182584
0.2%
4.897067971 × 10181038
0.1%
4.890424427 × 10181496
0.1%
Distinct693
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2025-10-04T13:01:43.973023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length43
Median length36
Mean length23.132597
Min length13

Characters and Unicode

Total characters29995460
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfraud_Rippin, Kub and Mann
2nd rowfraud_Heller, Gutmann and Zieme
3rd rowfraud_Lind-Buckridge
4th rowfraud_Kutch, Hermiston and Farrell
5th rowfraud_Keeling-Crist
ValueCountFrequency (%)
and474111
 
15.7%
llc97780
 
3.2%
inc91939
 
3.0%
sons73145
 
2.4%
ltd70853
 
2.3%
plc66475
 
2.2%
group50447
 
1.7%
fraud_kutch10560
 
0.3%
fraud_schaefer9394
 
0.3%
fraud_streich9250
 
0.3%
Other values (804)2069403
68.4%
2025-10-04T13:01:44.705594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a2910697
 
9.7%
r2695758
 
9.0%
d2139780
 
7.1%
e1865710
 
6.2%
u1857912
 
6.2%
n1768848
 
5.9%
1726682
 
5.8%
f1397378
 
4.7%
_1296675
 
4.3%
o1129340
 
3.8%
Other values (45)11206680
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)29995460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a2910697
 
9.7%
r2695758
 
9.0%
d2139780
 
7.1%
e1865710
 
6.2%
u1857912
 
6.2%
n1768848
 
5.9%
1726682
 
5.8%
f1397378
 
4.7%
_1296675
 
4.3%
o1129340
 
3.8%
Other values (45)11206680
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)29995460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a2910697
 
9.7%
r2695758
 
9.0%
d2139780
 
7.1%
e1865710
 
6.2%
u1857912
 
6.2%
n1768848
 
5.9%
1726682
 
5.8%
f1397378
 
4.7%
_1296675
 
4.3%
o1129340
 
3.8%
Other values (45)11206680
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)29995460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a2910697
 
9.7%
r2695758
 
9.0%
d2139780
 
7.1%
e1865710
 
6.2%
u1857912
 
6.2%
n1768848
 
5.9%
1726682
 
5.8%
f1397378
 
4.7%
_1296675
 
4.3%
o1129340
 
3.8%
Other values (45)11206680
37.4%

category
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
gas_transport
131659 
grocery_pos
123638 
home
123115 
shopping_pos
116672 
kids_pets
113035 
Other values (9)
688556 

Length

Max length14
Median length12
Mean length10.526079
Min length4

Characters and Unicode

Total characters13648903
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmisc_net
2nd rowgrocery_pos
3rd rowentertainment
4th rowgas_transport
5th rowmisc_pos

Common Values

ValueCountFrequency (%)
gas_transport131659
10.2%
grocery_pos123638
9.5%
home123115
9.5%
shopping_pos116672
9.0%
kids_pets113035
8.7%
shopping_net97543
7.5%
entertainment94014
7.3%
food_dining91461
 
7.1%
personal_care90758
 
7.0%
health_fitness85879
 
6.6%
Other values (4)228901
17.7%

Length

2025-10-04T13:01:44.853239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gas_transport131659
10.2%
grocery_pos123638
9.5%
home123115
9.5%
shopping_pos116672
9.0%
kids_pets113035
8.7%
shopping_net97543
7.5%
entertainment94014
7.3%
food_dining91461
 
7.1%
personal_care90758
 
7.0%
health_fitness85879
 
6.6%
Other values (4)228901
17.7%

Most occurring characters

ValueCountFrequency (%)
s1429026
10.5%
e1287345
9.4%
o1231724
9.0%
n1193757
8.7%
p1083847
 
7.9%
t1076942
 
7.9%
_1039039
 
7.6%
r917535
 
6.7%
i833007
 
6.1%
a665234
 
4.9%
Other values (10)2891447
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)13648903
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s1429026
10.5%
e1287345
9.4%
o1231724
9.0%
n1193757
8.7%
p1083847
 
7.9%
t1076942
 
7.9%
_1039039
 
7.6%
r917535
 
6.7%
i833007
 
6.1%
a665234
 
4.9%
Other values (10)2891447
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13648903
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s1429026
10.5%
e1287345
9.4%
o1231724
9.0%
n1193757
8.7%
p1083847
 
7.9%
t1076942
 
7.9%
_1039039
 
7.6%
r917535
 
6.7%
i833007
 
6.1%
a665234
 
4.9%
Other values (10)2891447
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13648903
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s1429026
10.5%
e1287345
9.4%
o1231724
9.0%
n1193757
8.7%
p1083847
 
7.9%
t1076942
 
7.9%
_1039039
 
7.6%
r917535
 
6.7%
i833007
 
6.1%
a665234
 
4.9%
Other values (10)2891447
21.2%

amt
Real number (ℝ)

Skewed 

Distinct52928
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.351035
Minimum1
Maximum28948.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2025-10-04T13:01:45.104897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.44
Q19.65
median47.52
Q383.14
95-th percentile196.31
Maximum28948.9
Range28947.9
Interquartile range (IQR)73.49

Descriptive statistics

Standard deviation160.31604
Coefficient of variation (CV)2.2788014
Kurtosis4545.645
Mean70.351035
Median Absolute Deviation (MAD)37.5
Skewness42.277874
Sum91222429
Variance25701.232
MonotonicityNot monotonic
2025-10-04T13:01:45.302681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.14542
 
< 0.1%
1.04538
 
< 0.1%
1.25535
 
< 0.1%
1.02533
 
< 0.1%
1.01523
 
< 0.1%
1.05519
 
< 0.1%
1.2516
 
< 0.1%
1.23515
 
< 0.1%
1.08512
 
< 0.1%
1.11509
 
< 0.1%
Other values (52918)1291433
99.6%
ValueCountFrequency (%)
1222
< 0.1%
1.01523
< 0.1%
1.02533
< 0.1%
1.03499
< 0.1%
1.04538
< 0.1%
1.05519
< 0.1%
1.06471
< 0.1%
1.07498
< 0.1%
1.08512
< 0.1%
1.09496
< 0.1%
ValueCountFrequency (%)
28948.91
< 0.1%
27390.121
< 0.1%
27119.771
< 0.1%
26544.121
< 0.1%
25086.941
< 0.1%
17897.241
< 0.1%
15305.951
< 0.1%
15047.031
< 0.1%
15034.181
< 0.1%
14849.741
< 0.1%

first
Text

Distinct352
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2025-10-04T13:01:45.679560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.0804319
Min length3

Characters and Unicode

Total characters7884344
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJennifer
2nd rowStephanie
3rd rowEdward
4th rowJeremy
5th rowTyler
ValueCountFrequency (%)
christopher26669
 
2.1%
robert21667
 
1.7%
jessica20581
 
1.6%
james20039
 
1.5%
michael20009
 
1.5%
david19965
 
1.5%
jennifer16940
 
1.3%
william16371
 
1.3%
mary16346
 
1.3%
john16325
 
1.3%
Other values (342)1101763
85.0%
2025-10-04T13:01:46.059816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a1007700
 
12.8%
e860878
 
10.9%
i618247
 
7.8%
n614453
 
7.8%
r607072
 
7.7%
l388220
 
4.9%
h344993
 
4.4%
s324237
 
4.1%
t311569
 
4.0%
o268849
 
3.4%
Other values (39)2538126
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)7884344
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1007700
 
12.8%
e860878
 
10.9%
i618247
 
7.8%
n614453
 
7.8%
r607072
 
7.7%
l388220
 
4.9%
h344993
 
4.4%
s324237
 
4.1%
t311569
 
4.0%
o268849
 
3.4%
Other values (39)2538126
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7884344
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1007700
 
12.8%
e860878
 
10.9%
i618247
 
7.8%
n614453
 
7.8%
r607072
 
7.7%
l388220
 
4.9%
h344993
 
4.4%
s324237
 
4.1%
t311569
 
4.0%
o268849
 
3.4%
Other values (39)2538126
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7884344
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1007700
 
12.8%
e860878
 
10.9%
i618247
 
7.8%
n614453
 
7.8%
r607072
 
7.7%
l388220
 
4.9%
h344993
 
4.4%
s324237
 
4.1%
t311569
 
4.0%
o268849
 
3.4%
Other values (39)2538126
32.2%

last
Text

Distinct481
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2025-10-04T13:01:46.396752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length10
Mean length6.1111774
Min length2

Characters and Unicode

Total characters7924211
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBanks
2nd rowGill
3rd rowSanchez
4th rowWhite
5th rowGarcia
ValueCountFrequency (%)
smith28794
 
2.2%
williams23605
 
1.8%
davis21910
 
1.7%
johnson20034
 
1.5%
rodriguez17394
 
1.3%
martinez14805
 
1.1%
jones13976
 
1.1%
lewis12753
 
1.0%
gonzalez11799
 
0.9%
miller11698
 
0.9%
Other values (471)1119907
86.4%
2025-10-04T13:01:46.853447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e786302
 
9.9%
r658748
 
8.3%
a648005
 
8.2%
n609178
 
7.7%
o583517
 
7.4%
l489180
 
6.2%
s487668
 
6.2%
i435378
 
5.5%
t288591
 
3.6%
h228981
 
2.9%
Other values (38)2708663
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)7924211
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e786302
 
9.9%
r658748
 
8.3%
a648005
 
8.2%
n609178
 
7.7%
o583517
 
7.4%
l489180
 
6.2%
s487668
 
6.2%
i435378
 
5.5%
t288591
 
3.6%
h228981
 
2.9%
Other values (38)2708663
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7924211
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e786302
 
9.9%
r658748
 
8.3%
a648005
 
8.2%
n609178
 
7.7%
o583517
 
7.4%
l489180
 
6.2%
s487668
 
6.2%
i435378
 
5.5%
t288591
 
3.6%
h228981
 
2.9%
Other values (38)2708663
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7924211
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e786302
 
9.9%
r658748
 
8.3%
a648005
 
8.2%
n609178
 
7.7%
o583517
 
7.4%
l489180
 
6.2%
s487668
 
6.2%
i435378
 
5.5%
t288591
 
3.6%
h228981
 
2.9%
Other values (38)2708663
34.2%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
F
709863 
M
586812 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1296675
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
F709863
54.7%
M586812
45.3%

Length

2025-10-04T13:01:46.952711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-04T13:01:47.008312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
f709863
54.7%
m586812
45.3%

Most occurring characters

ValueCountFrequency (%)
F709863
54.7%
M586812
45.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1296675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F709863
54.7%
M586812
45.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1296675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F709863
54.7%
M586812
45.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1296675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F709863
54.7%
M586812
45.3%

street
Text

Distinct983
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2025-10-04T13:01:47.263612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length29
Mean length22.229027
Min length12

Characters and Unicode

Total characters28823823
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row561 Perry Cove
2nd row43039 Riley Greens Suite 393
3rd row594 White Dale Suite 530
4th row9443 Cynthia Court Apt. 038
5th row408 Bradley Rest
ValueCountFrequency (%)
apt327791
 
6.4%
suite305467
 
5.9%
island22954
 
0.4%
michael18967
 
0.4%
common17978
 
0.3%
station17957
 
0.3%
islands17917
 
0.3%
david17476
 
0.3%
brooks16991
 
0.3%
fields16321
 
0.3%
Other values (1940)4376722
84.9%
2025-10-04T13:01:47.670574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3859866
 
13.4%
e1792676
 
6.2%
a1454190
 
5.0%
i1296969
 
4.5%
t1248091
 
4.3%
r1103208
 
3.8%
n1066149
 
3.7%
s1034564
 
3.6%
l889594
 
3.1%
o875571
 
3.0%
Other values (52)14202945
49.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)28823823
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3859866
 
13.4%
e1792676
 
6.2%
a1454190
 
5.0%
i1296969
 
4.5%
t1248091
 
4.3%
r1103208
 
3.8%
n1066149
 
3.7%
s1034564
 
3.6%
l889594
 
3.1%
o875571
 
3.0%
Other values (52)14202945
49.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)28823823
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3859866
 
13.4%
e1792676
 
6.2%
a1454190
 
5.0%
i1296969
 
4.5%
t1248091
 
4.3%
r1103208
 
3.8%
n1066149
 
3.7%
s1034564
 
3.6%
l889594
 
3.1%
o875571
 
3.0%
Other values (52)14202945
49.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)28823823
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3859866
 
13.4%
e1792676
 
6.2%
a1454190
 
5.0%
i1296969
 
4.5%
t1248091
 
4.3%
r1103208
 
3.8%
n1066149
 
3.7%
s1034564
 
3.6%
l889594
 
3.1%
o875571
 
3.0%
Other values (52)14202945
49.3%

city
Text

Distinct894
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2025-10-04T13:01:47.972593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length21
Mean length8.6522459
Min length3

Characters and Unicode

Total characters11219151
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMoravian Falls
2nd rowOrient
3rd rowMalad City
4th rowBoulder
5th rowDoe Hill
ValueCountFrequency (%)
city21314
 
1.3%
west19473
 
1.2%
north14425
 
0.9%
saint14363
 
0.9%
falls12794
 
0.8%
new11842
 
0.7%
mount11375
 
0.7%
lake11249
 
0.7%
san10260
 
0.6%
springs8727
 
0.5%
Other values (918)1482445
91.6%
2025-10-04T13:01:48.380674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e1090254
 
9.7%
a935089
 
8.3%
n821831
 
7.3%
o817806
 
7.3%
l781662
 
7.0%
r748921
 
6.7%
i704285
 
6.3%
t598490
 
5.3%
s446306
 
4.0%
321592
 
2.9%
Other values (42)3952915
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)11219151
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1090254
 
9.7%
a935089
 
8.3%
n821831
 
7.3%
o817806
 
7.3%
l781662
 
7.0%
r748921
 
6.7%
i704285
 
6.3%
t598490
 
5.3%
s446306
 
4.0%
321592
 
2.9%
Other values (42)3952915
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11219151
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1090254
 
9.7%
a935089
 
8.3%
n821831
 
7.3%
o817806
 
7.3%
l781662
 
7.0%
r748921
 
6.7%
i704285
 
6.3%
t598490
 
5.3%
s446306
 
4.0%
321592
 
2.9%
Other values (42)3952915
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11219151
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1090254
 
9.7%
a935089
 
8.3%
n821831
 
7.3%
o817806
 
7.3%
l781662
 
7.0%
r748921
 
6.7%
i704285
 
6.3%
t598490
 
5.3%
s446306
 
4.0%
321592
 
2.9%
Other values (42)3952915
35.2%

state
Text

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2025-10-04T13:01:48.562189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2593350
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNC
2nd rowWA
3rd rowID
4th rowMT
5th rowVA
ValueCountFrequency (%)
tx94876
 
7.3%
ny83501
 
6.4%
pa79847
 
6.2%
ca56360
 
4.3%
oh46480
 
3.6%
mi46154
 
3.6%
il43252
 
3.3%
fl42671
 
3.3%
al40989
 
3.2%
mo38403
 
3.0%
Other values (41)724142
55.8%
2025-10-04T13:01:48.852917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A355776
13.7%
N284464
 
11.0%
M220694
 
8.5%
I181993
 
7.0%
T154353
 
6.0%
L147877
 
5.7%
O144031
 
5.6%
C141011
 
5.4%
Y131298
 
5.1%
X94876
 
3.7%
Other values (14)736977
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)2593350
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A355776
13.7%
N284464
 
11.0%
M220694
 
8.5%
I181993
 
7.0%
T154353
 
6.0%
L147877
 
5.7%
O144031
 
5.6%
C141011
 
5.4%
Y131298
 
5.1%
X94876
 
3.7%
Other values (14)736977
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2593350
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A355776
13.7%
N284464
 
11.0%
M220694
 
8.5%
I181993
 
7.0%
T154353
 
6.0%
L147877
 
5.7%
O144031
 
5.6%
C141011
 
5.4%
Y131298
 
5.1%
X94876
 
3.7%
Other values (14)736977
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2593350
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A355776
13.7%
N284464
 
11.0%
M220694
 
8.5%
I181993
 
7.0%
T154353
 
6.0%
L147877
 
5.7%
O144031
 
5.6%
C141011
 
5.4%
Y131298
 
5.1%
X94876
 
3.7%
Other values (14)736977
28.4%

zip
Real number (ℝ)

High correlation 

Distinct970
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48800.671
Minimum1257
Maximum99783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2025-10-04T13:01:48.962412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1257
5-th percentile7208
Q126237
median48174
Q372042
95-th percentile94569
Maximum99783
Range98526
Interquartile range (IQR)45805

Descriptive statistics

Standard deviation26893.222
Coefficient of variation (CV)0.55108305
Kurtosis-1.0964493
Mean48800.671
Median Absolute Deviation (MAD)23068
Skewness0.079680758
Sum6.327861 × 1010
Variance7.2324542 × 108
MonotonicityNot monotonic
2025-10-04T13:01:49.086275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
737543646
 
0.3%
341123613
 
0.3%
480883597
 
0.3%
825143527
 
0.3%
496283123
 
0.2%
154843123
 
0.2%
851733119
 
0.2%
298193117
 
0.2%
387613113
 
0.2%
54613112
 
0.2%
Other values (960)1263585
97.4%
ValueCountFrequency (%)
12572023
0.2%
13301031
 
0.1%
1535515
 
< 0.1%
15451024
 
0.1%
1612519
 
< 0.1%
18432597
0.2%
18442058
0.2%
2180519
 
< 0.1%
26302090
0.2%
2908550
 
< 0.1%
ValueCountFrequency (%)
997831568
0.1%
9974712
 
< 0.1%
99746540
 
< 0.1%
993232572
0.2%
991603030
0.2%
9911615
 
< 0.1%
991131047
 
0.1%
990332458
0.2%
98836524
 
< 0.1%
98665500
 
< 0.1%

lat
Real number (ℝ)

High correlation 

Distinct968
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.537622
Minimum20.0271
Maximum66.6933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2025-10-04T13:01:49.204197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20.0271
5-th percentile29.8826
Q134.6205
median39.3543
Q341.9404
95-th percentile45.8433
Maximum66.6933
Range46.6662
Interquartile range (IQR)7.3199

Descriptive statistics

Standard deviation5.0758084
Coefficient of variation (CV)0.13171047
Kurtosis0.81296795
Mean38.537622
Median Absolute Deviation (MAD)3.3597
Skewness-0.18602768
Sum49970771
Variance25.763831
MonotonicityNot monotonic
2025-10-04T13:01:49.319356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.3853646
 
0.3%
26.11843613
 
0.3%
42.51643597
 
0.3%
43.00483527
 
0.3%
39.89363123
 
0.2%
44.59953123
 
0.2%
33.28873119
 
0.2%
34.03263117
 
0.2%
33.47833113
 
0.2%
44.33463112
 
0.2%
Other values (958)1263585
97.4%
ValueCountFrequency (%)
20.02711527
0.1%
20.08271032
 
0.1%
24.65572584
0.2%
26.11843613
0.3%
26.3304542
 
< 0.1%
26.3771518
 
< 0.1%
26.42153038
0.2%
26.47222524
0.2%
26.5291549
0.1%
26.69391027
 
0.1%
ValueCountFrequency (%)
66.693312
 
< 0.1%
65.6899540
 
< 0.1%
64.75561568
0.1%
48.88783030
0.2%
48.88562066
0.2%
48.83281533
0.1%
48.66691047
 
0.1%
48.60312973
0.2%
48.47862038
0.2%
48.343088
0.2%

long
Real number (ℝ)

High correlation 

Distinct969
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.226335
Minimum-165.6723
Maximum-67.9503
Zeros0
Zeros (%)0.0%
Negative1296675
Negative (%)100.0%
Memory size9.9 MiB
2025-10-04T13:01:49.438315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-165.6723
5-th percentile-119.0825
Q1-96.798
median-87.4769
Q3-80.158
95-th percentile-73.5112
Maximum-67.9503
Range97.722
Interquartile range (IQR)16.64

Descriptive statistics

Standard deviation13.759077
Coefficient of variation (CV)-0.15249513
Kurtosis1.8558923
Mean-90.226335
Median Absolute Deviation (MAD)8.1527
Skewness-1.1501077
Sum-1.1699423 × 108
Variance189.3122
MonotonicityNot monotonic
2025-10-04T13:01:49.548267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-98.07273646
 
0.3%
-81.73613613
 
0.3%
-82.98323597
 
0.3%
-108.89643527
 
0.3%
-79.78563123
 
0.2%
-86.21413123
 
0.2%
-111.09853119
 
0.2%
-82.20273117
 
0.2%
-90.51423113
 
0.2%
-73.0983112
 
0.2%
Other values (959)1263585
97.4%
ValueCountFrequency (%)
-165.67231568
0.1%
-156.292540
 
< 0.1%
-155.4881032
0.1%
-155.36971527
0.1%
-153.99412
 
< 0.1%
-124.44091043
0.1%
-124.21741547
0.1%
-124.15871031
0.1%
-124.14371526
0.1%
-123.97432036
0.2%
ValueCountFrequency (%)
-67.95032080
0.2%
-68.55651014
 
0.1%
-69.2675519
 
< 0.1%
-69.48282050
0.2%
-69.9576537
 
< 0.1%
-69.96563107
0.2%
-70.10319
 
< 0.1%
-70.2391036
 
0.1%
-70.30012090
0.2%
-70.34571527
0.1%

city_pop
Real number (ℝ)

Distinct879
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88824.441
Minimum23
Maximum2906700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2025-10-04T13:01:49.661035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile139
Q1743
median2456
Q320328
95-th percentile525713
Maximum2906700
Range2906677
Interquartile range (IQR)19585

Descriptive statistics

Standard deviation301956.36
Coefficient of variation (CV)3.3994738
Kurtosis37.614519
Mean88824.441
Median Absolute Deviation (MAD)2198
Skewness5.5938531
Sum1.1517643 × 1011
Variance9.1177644 × 1010
MonotonicityNot monotonic
2025-10-04T13:01:49.790686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6065496
 
0.4%
15957975130
 
0.4%
13129225075
 
0.4%
17664574
 
0.4%
2414533
 
0.3%
29067004168
 
0.3%
2760024155
 
0.3%
3024147
 
0.3%
9101484073
 
0.3%
1984067
 
0.3%
Other values (869)1251257
96.5%
ValueCountFrequency (%)
232049
0.2%
371013
 
0.1%
432034
0.2%
463040
0.2%
47511
 
< 0.1%
491054
 
0.1%
511016
 
0.1%
52518
 
< 0.1%
532610
0.2%
601045
 
0.1%
ValueCountFrequency (%)
29067004168
0.3%
25047002033
 
0.2%
2383912521
 
< 0.1%
15957975130
0.4%
15773852563
0.2%
15262063517
0.3%
14177938
 
< 0.1%
13824802056
0.2%
13129225075
0.4%
12633213629
0.3%

job
Text

Distinct494
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2025-10-04T13:01:50.032274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length59
Median length38
Mean length20.227102
Min length3

Characters and Unicode

Total characters26227978
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPsychologist, counselling
2nd rowSpecial educational needs teacher
3rd rowNature conservation officer
4th rowPatent attorney
5th rowDance movement psychotherapist
ValueCountFrequency (%)
engineer131756
 
4.6%
officer110915
 
3.9%
manager61124
 
2.1%
scientist55878
 
1.9%
designer52218
 
1.8%
surveyor49062
 
1.7%
teacher38126
 
1.3%
psychologist32600
 
1.1%
research29754
 
1.0%
editor28725
 
1.0%
Other values (456)2289024
79.5%
2025-10-04T13:01:50.403288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e2803032
 
10.7%
i2386346
 
9.1%
r2198669
 
8.4%
a1813638
 
6.9%
t1782302
 
6.8%
n1764769
 
6.7%
1582507
 
6.0%
o1491775
 
5.7%
s1444701
 
5.5%
c1323152
 
5.0%
Other values (43)7637087
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)26227978
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e2803032
 
10.7%
i2386346
 
9.1%
r2198669
 
8.4%
a1813638
 
6.9%
t1782302
 
6.8%
n1764769
 
6.7%
1582507
 
6.0%
o1491775
 
5.7%
s1444701
 
5.5%
c1323152
 
5.0%
Other values (43)7637087
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)26227978
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e2803032
 
10.7%
i2386346
 
9.1%
r2198669
 
8.4%
a1813638
 
6.9%
t1782302
 
6.8%
n1764769
 
6.7%
1582507
 
6.0%
o1491775
 
5.7%
s1444701
 
5.5%
c1323152
 
5.0%
Other values (43)7637087
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)26227978
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e2803032
 
10.7%
i2386346
 
9.1%
r2198669
 
8.4%
a1813638
 
6.9%
t1782302
 
6.8%
n1764769
 
6.7%
1582507
 
6.0%
o1491775
 
5.7%
s1444701
 
5.5%
c1323152
 
5.0%
Other values (43)7637087
29.1%

dob
Date

Distinct968
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
Minimum1924-10-30 00:00:00
Maximum2005-01-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-04T13:01:50.516706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:50.643323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

trans_num
Text

Unique 

Distinct1296675
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2025-10-04T13:01:52.241826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters41493600
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1296675 ?
Unique (%)100.0%

Sample

1st row0b242abb623afc578575680df30655b9
2nd row1f76529f8574734946361c461b024d99
3rd rowa1a22d70485983eac12b5b88dad1cf95
4th row6b849c168bdad6f867558c3793159a81
5th rowa41d7549acf90789359a9aa5346dcb46
ValueCountFrequency (%)
6b849c168bdad6f867558c3793159a811
 
< 0.1%
8f7c8e4ab7f25875d753b422917c98c91
 
< 0.1%
0b242abb623afc578575680df30655b91
 
< 0.1%
d2263b4459d544bedb14a6c0abf049181
 
< 0.1%
de289ff4302f93f9de1ebf1b0152a8ff1
 
< 0.1%
cc47f72c6ed03fbf25c724690c2aedfa1
 
< 0.1%
42d93aef20d9be92f38f4d1f04671b881
 
< 0.1%
0a2e7ce8ee5f33346f54f9a9605dd3351
 
< 0.1%
be0ac1fd89b5d91d5032c052d70e8ed21
 
< 0.1%
108c103b26f686c24c021aaf4210977e1
 
< 0.1%
Other values (1296665)1296665
> 99.9%
2025-10-04T13:01:53.788907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22596593
 
6.3%
92596375
 
6.3%
72595084
 
6.3%
42594676
 
6.3%
a2594103
 
6.3%
d2593816
 
6.3%
32593713
 
6.3%
f2593666
 
6.3%
52593098
 
6.2%
e2592759
 
6.2%
Other values (6)15549717
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)41493600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
22596593
 
6.3%
92596375
 
6.3%
72595084
 
6.3%
42594676
 
6.3%
a2594103
 
6.3%
d2593816
 
6.3%
32593713
 
6.3%
f2593666
 
6.3%
52593098
 
6.2%
e2592759
 
6.2%
Other values (6)15549717
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)41493600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
22596593
 
6.3%
92596375
 
6.3%
72595084
 
6.3%
42594676
 
6.3%
a2594103
 
6.3%
d2593816
 
6.3%
32593713
 
6.3%
f2593666
 
6.3%
52593098
 
6.2%
e2592759
 
6.2%
Other values (6)15549717
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)41493600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
22596593
 
6.3%
92596375
 
6.3%
72595084
 
6.3%
42594676
 
6.3%
a2594103
 
6.3%
d2593816
 
6.3%
32593713
 
6.3%
f2593666
 
6.3%
52593098
 
6.2%
e2592759
 
6.2%
Other values (6)15549717
37.5%

unix_time
Real number (ℝ)

High correlation 

Distinct1274823
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3492436 × 109
Minimum1.325376 × 109
Maximum1.3718168 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2025-10-04T13:01:53.924250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.325376 × 109
5-th percentile1.328672 × 109
Q11.3387507 × 109
median1.3492497 × 109
Q31.3593854 × 109
95-th percentile1.3698306 × 109
Maximum1.3718168 × 109
Range46440799
Interquartile range (IQR)20634633

Descriptive statistics

Standard deviation12841278
Coefficient of variation (CV)0.0095173904
Kurtosis-1.0875405
Mean1.3492436 × 109
Median Absolute Deviation (MAD)10358807
Skewness0.0033779498
Sum1.7495305 × 1015
Variance1.6489843 × 1014
MonotonicityIncreasing
2025-10-04T13:01:54.047321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13351105214
 
< 0.1%
13701772274
 
< 0.1%
13700506674
 
< 0.1%
13361894413
 
< 0.1%
13440748583
 
< 0.1%
13532798293
 
< 0.1%
13289265463
 
< 0.1%
13711942073
 
< 0.1%
13436685203
 
< 0.1%
13604245313
 
< 0.1%
Other values (1274813)1296642
> 99.9%
ValueCountFrequency (%)
13253760181
< 0.1%
13253760441
< 0.1%
13253760511
< 0.1%
13253760761
< 0.1%
13253761861
< 0.1%
13253762481
< 0.1%
13253762821
< 0.1%
13253763081
< 0.1%
13253763181
< 0.1%
13253763611
< 0.1%
ValueCountFrequency (%)
13718168171
< 0.1%
13718168161
< 0.1%
13718167521
< 0.1%
13718167391
< 0.1%
13718167281
< 0.1%
13718166961
< 0.1%
13718166831
< 0.1%
13718166561
< 0.1%
13718165621
< 0.1%
13718165221
< 0.1%

merch_lat
Real number (ℝ)

High correlation 

Distinct1247805
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.537338
Minimum19.027785
Maximum67.510267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2025-10-04T13:01:54.179096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19.027785
5-th percentile29.751653
Q134.733572
median39.36568
Q341.957164
95-th percentile46.00353
Maximum67.510267
Range48.482482
Interquartile range (IQR)7.223592

Descriptive statistics

Standard deviation5.1097884
Coefficient of variation (CV)0.13259318
Kurtosis0.79599391
Mean38.537338
Median Absolute Deviation (MAD)3.397536
Skewness-0.18191543
Sum49970403
Variance26.109937
MonotonicityNot monotonic
2025-10-04T13:01:54.303781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.9851664
 
< 0.1%
34.0484394
 
< 0.1%
40.1601834
 
< 0.1%
38.7140964
 
< 0.1%
38.8950984
 
< 0.1%
41.8470834
 
< 0.1%
42.9205844
 
< 0.1%
33.9951524
 
< 0.1%
43.3730764
 
< 0.1%
43.3274954
 
< 0.1%
Other values (1247795)1296635
> 99.9%
ValueCountFrequency (%)
19.0277851
< 0.1%
19.0278041
< 0.1%
19.0297981
< 0.1%
19.0312421
< 0.1%
19.0322771
< 0.1%
19.0332881
< 0.1%
19.0342821
< 0.1%
19.0346871
< 0.1%
19.0354721
< 0.1%
19.0363121
< 0.1%
ValueCountFrequency (%)
67.5102671
< 0.1%
67.4415181
< 0.1%
67.3970181
< 0.1%
67.1881111
< 0.1%
67.0642771
< 0.1%
66.8351741
< 0.1%
66.6829051
< 0.1%
66.673551
< 0.1%
66.6646731
< 0.1%
66.6592421
< 0.1%

merch_long
Real number (ℝ)

High correlation 

Distinct1275745
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.226465
Minimum-166.67124
Maximum-66.950902
Zeros0
Zeros (%)0.0%
Negative1296675
Negative (%)100.0%
Memory size9.9 MiB
2025-10-04T13:01:54.420608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-166.67124
5-th percentile-119.33009
Q1-96.897276
median-87.438392
Q3-80.236796
95-th percentile-73.354218
Maximum-66.950902
Range99.72034
Interquartile range (IQR)16.660479

Descriptive statistics

Standard deviation13.771091
Coefficient of variation (CV)-0.15262806
Kurtosis1.8484792
Mean-90.226465
Median Absolute Deviation (MAD)8.227889
Skewness-1.1469599
Sum-1.169944 × 108
Variance189.64294
MonotonicityNot monotonic
2025-10-04T13:01:54.535101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-81.2191894
 
< 0.1%
-74.6182694
 
< 0.1%
-87.1164144
 
< 0.1%
-80.9405243
 
< 0.1%
-94.7310383
 
< 0.1%
-73.2927723
 
< 0.1%
-82.3714193
 
< 0.1%
-84.0175573
 
< 0.1%
-83.9000643
 
< 0.1%
-82.6446963
 
< 0.1%
Other values (1275735)1296642
> 99.9%
ValueCountFrequency (%)
-166.6712421
< 0.1%
-166.6701321
< 0.1%
-166.6696381
< 0.1%
-166.6661791
< 0.1%
-166.6648281
< 0.1%
-166.6628881
< 0.1%
-166.6619681
< 0.1%
-166.6592771
< 0.1%
-166.6578341
< 0.1%
-166.6571741
< 0.1%
ValueCountFrequency (%)
-66.9509021
< 0.1%
-66.9559961
< 0.1%
-66.956541
< 0.1%
-66.9586591
< 0.1%
-66.9587511
< 0.1%
-66.9591781
< 0.1%
-66.9619231
< 0.1%
-66.9629131
< 0.1%
-66.9639181
< 0.1%
-66.9639751
< 0.1%

is_fraud
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
0
1289169 
1
 
7506

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1296675
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01289169
99.4%
17506
 
0.6%

Length

2025-10-04T13:01:54.633793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-04T13:01:54.687706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01289169
99.4%
17506
 
0.6%

Most occurring characters

ValueCountFrequency (%)
01289169
99.4%
17506
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1296675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01289169
99.4%
17506
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1296675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01289169
99.4%
17506
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1296675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01289169
99.4%
17506
 
0.6%

Interactions

2025-10-04T13:01:30.637680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:10.536369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:12.557508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:14.835556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:17.123239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:19.495108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:21.577799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:23.646209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:25.690888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:28.554585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:30.860143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:10.736746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:12.745938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:15.097295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:17.355675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:19.699183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:21.792248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:23.854867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:25.892423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:28.762990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:31.059550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:10.942653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:12.932605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:15.388198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:17.565185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:19.903152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:21.998134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:24.064156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:26.461182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:28.987856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:31.265267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:11.157372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:13.139107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:15.659266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:17.781301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:20.115908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:22.208734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:24.273317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:26.726196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:29.198503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:31.468995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:11.364623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:13.331914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:15.935888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:17.994789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:20.320130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:22.419450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:24.478326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:26.995335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:29.405761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:31.672869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:11.560091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:13.523408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:16.126477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:18.426357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:20.533687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:22.622658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:24.682913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:27.240213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:29.609426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:31.936594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:11.757257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:13.788138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:16.334616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:18.628733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:20.746042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:22.823209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:24.880736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:27.490869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:29.813242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:32.184183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:11.955771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:14.061731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:16.527247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:18.842000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:20.954557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:23.032875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:25.085715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:27.778236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:30.025453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:32.387386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:12.169831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:14.335314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:16.721303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:19.055404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:21.159904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:23.239788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:25.293097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:28.058277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:30.225833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:32.581507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:12.366365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:14.590236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:16.911433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:19.273794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:21.355115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:23.441855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:25.489395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:28.325272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:01:30.429533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-04T13:01:54.743880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Unnamed: 0amtcategorycc_numcity_popgenderis_fraudlatlongmerch_latmerch_longunix_timezip
Unnamed: 01.0000.0010.0010.002-0.0030.0000.0190.001-0.0010.001-0.0011.0000.001
amt0.0011.0000.020-0.001-0.0240.0000.0000.012-0.0000.0120.0000.0010.001
category0.0010.0201.0000.0090.0140.0540.0710.0110.0090.0110.0090.0010.011
cc_num0.002-0.0010.0091.0000.0490.0510.006-0.004-0.013-0.004-0.0130.0020.013
city_pop-0.003-0.0240.0140.0491.0000.0890.004-0.2650.087-0.2640.086-0.003-0.040
gender0.0000.0000.0540.0510.0891.0000.0080.1010.0910.1030.0820.0000.119
is_fraud0.0190.0000.0710.0060.0040.0081.0000.0080.0060.0080.0050.0180.005
lat0.0010.0120.011-0.004-0.2650.1010.0081.0000.1060.9910.1050.001-0.162
long-0.001-0.0000.009-0.0130.0870.0910.0060.1061.0000.1060.998-0.001-0.959
merch_lat0.0010.0120.011-0.004-0.2640.1030.0080.9910.1061.0000.1040.001-0.162
merch_long-0.0010.0000.009-0.0130.0860.0820.0050.1050.9980.1041.000-0.001-0.957
unix_time1.0000.0010.0010.002-0.0030.0000.0180.001-0.0010.001-0.0011.0000.001
zip0.0010.0010.0110.013-0.0400.1190.005-0.162-0.959-0.162-0.9570.0011.000

Missing values

2025-10-04T13:01:33.895885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-04T13:01:37.230438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0trans_date_trans_timecc_nummerchantcategoryamtfirstlastgenderstreetcitystateziplatlongcity_popjobdobtrans_numunix_timemerch_latmerch_longis_fraud
002019-01-01 00:00:182703186189652095fraud_Rippin, Kub and Mannmisc_net4.97JenniferBanksF561 Perry CoveMoravian FallsNC2865436.0788-81.17813495Psychologist, counselling1988-03-090b242abb623afc578575680df30655b9132537601836.011293-82.0483150
112019-01-01 00:00:44630423337322fraud_Heller, Gutmann and Ziemegrocery_pos107.23StephanieGillF43039 Riley Greens Suite 393OrientWA9916048.8878-118.2105149Special educational needs teacher1978-06-211f76529f8574734946361c461b024d99132537604449.159047-118.1864620
222019-01-01 00:00:5138859492057661fraud_Lind-Buckridgeentertainment220.11EdwardSanchezM594 White Dale Suite 530Malad CityID8325242.1808-112.26204154Nature conservation officer1962-01-19a1a22d70485983eac12b5b88dad1cf95132537605143.150704-112.1544810
332019-01-01 00:01:163534093764340240fraud_Kutch, Hermiston and Farrellgas_transport45.00JeremyWhiteM9443 Cynthia Court Apt. 038BoulderMT5963246.2306-112.11381939Patent attorney1967-01-126b849c168bdad6f867558c3793159a81132537607647.034331-112.5610710
442019-01-01 00:03:06375534208663984fraud_Keeling-Cristmisc_pos41.96TylerGarciaM408 Bradley RestDoe HillVA2443338.4207-79.462999Dance movement psychotherapist1986-03-28a41d7549acf90789359a9aa5346dcb46132537618638.674999-78.6324590
552019-01-01 00:04:084767265376804500fraud_Stroman, Hudson and Erdmangas_transport94.63JenniferConnerF4655 David IslandDublinPA1891740.3750-75.20452158Transport planner1961-06-19189a841a0a8ba03058526bcfe566aab5132537624840.653382-76.1526670
662019-01-01 00:04:4230074693890476fraud_Rowe-Vandervortgrocery_net44.54KelseyRichardsF889 Sarah Station Suite 624HolcombKS6785137.9931-100.98932691Arboriculturist1993-08-1683ec1cc84142af6e2acf10c44949e720132537628237.162705-100.1533700
772019-01-01 00:05:086011360759745864fraud_Corwin-Collinsgas_transport71.65StevenWilliamsM231 Flores Pass Suite 720EdinburgVA2282438.8432-78.60036018Designer, multimedia1947-08-216d294ed2cc447d2c71c7171a3d54967c132537630838.948089-78.5402960
882019-01-01 00:05:184922710831011201fraud_Herzog Ltdmisc_pos4.27HeatherChaseF6888 Hicks Stream Suite 954ManorPA1566540.3359-79.66071472Public affairs consultant1941-03-07fc28024ce480f8ef21a32d64c93a29f5132537631840.351813-79.9581460
992019-01-01 00:06:012720830304681674fraud_Schoen, Kuphal and Nitzschegrocery_pos198.39MelissaAguilarF21326 Taylor Squares Suite 708ClarksvilleTN3704036.5220-87.3490151785Pathologist1974-03-283b9014ea8fb80bd65de0b1463b00b00e132537636137.179198-87.4853810
Unnamed: 0trans_date_trans_timecc_nummerchantcategoryamtfirstlastgenderstreetcitystateziplatlongcity_popjobdobtrans_numunix_timemerch_latmerch_longis_fraud
129666512966652020-06-21 12:08:42213193596103206fraud_Gulgowski LLChome72.17JamesHuntM7369 Gabriel TunnelPointe Aux PinsMI4977545.7549-84.447095Electrical engineer1994-02-09108c103b26f686c24c021aaf4210977e137181652244.938461-83.9962340
129666612966662020-06-21 12:09:224587657402165341815fraud_Hyatt, Russel and Gleichnerhealth_fitness7.30AmberLewisF6296 John Keys Suite 858Pembroke TownshipIL6095841.0646-87.59172135Psychotherapist, child2004-05-0837a18c6fb0c5c722b6339ffedc82f55a137181656240.556811-88.0923390
129666712966672020-06-21 12:10:564822367783500458fraud_Hahn, Douglas and Schowaltertravel19.71ChristopherFarrellM97070 Anderson LandHaines CityFL3384428.0758-81.592933804Exercise physiologist1991-01-0134e72e0a659a6c8f4a20ee65594f3a7d137181665627.465871-81.5118040
129666812966682020-06-21 12:11:23213141712584544fraud_Metz, Russel and Metzkids_pets100.85MargaretCurtisF742 Oneill ShoreFlorenceMS3907332.1530-90.121719685Fine artist1984-12-240d86d8c17638d7eff77db9c6a878b477137181668331.377697-90.5284500
129666912966692020-06-21 12:11:364400011257587661852fraud_Stiedemann Incmisc_pos37.38MarissaPowellF474 Allen HavenNorth LoupNE6885941.4972-98.7858509Nurse, children's1980-09-159a7ea2625cf8303efe34e3c09546868f137181669641.728638-99.0396600
129667012966702020-06-21 12:12:0830263540414123fraud_Reichel Incentertainment15.56ErikPattersonM162 Jessica Row Apt. 072HatchUT8473537.7175-112.4777258Geoscientist1961-11-24440b587732da4dc1a6395aba5fb41669137181672836.841266-111.6907650
129667112966712020-06-21 12:12:196011149206456997fraud_Abernathy and Sonsfood_dining51.70JeffreyWhiteM8617 Holmes Terrace Suite 651TuscaroraMD2179039.2667-77.5101100Production assistant, television1979-12-11278000d2e0d2277d1de2f890067dcc0a137181673938.906881-78.2465280
129667212966722020-06-21 12:12:323514865930894695fraud_Stiedemann Ltdfood_dining105.93ChristopherCastanedaM1632 Cohen Drive Suite 639High Rolls Mountain ParkNM8832532.9396-105.8189899Naval architect1967-08-30483f52fe67fabef353d552c1e662974c137181675233.619513-105.1305290
129667312966732020-06-21 12:13:362720012583106919fraud_Reinger, Weissnat and Strosinfood_dining74.90JosephMurrayM42933 Ryan UnderpassMandersonSD5775643.3526-102.54111126Volunteer coordinator1980-08-18d667cdcbadaaed3da3f4020e83591c83137181681642.788940-103.2411600
129667412966742020-06-21 12:13:374292902571056973207fraud_Langosh, Wintheiser and Hyattfood_dining4.30JeffreySmithM135 Joseph MountainsSulaMT5987145.8433-113.8748218Therapist, horticultural1995-08-168f7c8e4ab7f25875d753b422917c98c9137181681746.565983-114.1861100